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View conditional_dist.ipynb
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AidanRocke / normal_approximation
Created Mar 14, 2018
approximating a uniform distribution with a normal distribution
View normal_approximation
"""
Created on Tue Mar 13 19:17:39 2018
@author: aidanrocke
"""
import tensorflow as tf
import numpy as np
@AidanRocke
AidanRocke / accumulated_gradients.ipynb
Last active May 9, 2018
Minimal example of accumulated gradients in TensorFlow
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AidanRocke / google_translate_challenge.py
Created Jun 8, 2018 — forked from kingjr/google_translate_challenge.py
Find simplest google translate request that generate the maximum number of unique words
View google_translate_challenge.py
from googletrans import Translator
from itertools import product
from pandas import DataFrame, read_csv
import numpy as np
import string
import time
import os
# Get google translator object
translator = Translator()
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AidanRocke / fixed_accumulated_gradients.ipynb
Created Aug 20, 2018
Correct example of accumulated gradients
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AidanRocke / computing_gradients.tf
Created Nov 29, 2018
Computing gradient norms with TensorFlow
View computing_gradients.tf
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 29 13:06:26 2018
@author: aidanrockea
"""
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
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AidanRocke / local_convex_test.jl
Last active Mar 6, 2019
A test for local convexity
View local_convex_test.jl
using LinearAlgebra
using Random
function local_convex_test(N)
#srand(1234)
sum = 0.0
View circuit_complexity.jl
using LinearAlgebra
using Random
function circuit_complexity(N)
radii = zeros(1,N);
for i=1:N
@AidanRocke
AidanRocke / crazy_paths.jl
Last active Mar 27, 2019
A generative model based on the 2/3 power law
View crazy_paths.jl
using Random
function crazy_paths(N,delta_t)
ddx, dx = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0)
ddy, dy = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0)
x, y = 10*(2*rand(N+1) .-1.0), 10*(2*rand(N+1) .-1.0)
View monotone_approx.jl
using Distributions
using Statistics
function monotone_approx(N::Int64,n_trials::Int64,n::Int64)
"""
inputs:
N: the range of U([-N,N])
n_trials: the number of times we generate random vectors
n: the 'dimension' of the random vector